Linking Aid to the Sustainable Development Goals - a machine learning approach:
Official Development Assistance amounted USD 146.6 billions in 2017 but do we know how much of this aid contributed to the Sustainable Development Goals (SDGs)? And to what SDG in particular? This paper present a new methodology using machine learning designed to link project-based flows to the Sust...
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| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2019
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| Schriftenreihe: | OECD Development Co-operation Working Papers
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| Schlagworte: | |
| Online-Zugang: | UBA01 UBG01 UEI01 UER01 UPA01 UBR01 UBW01 FFW01 FNU01 EUV01 FRO01 FHR01 FHN01 TUM01 FHI01 UBM01 Volltext |
| Zusammenfassung: | Official Development Assistance amounted USD 146.6 billions in 2017 but do we know how much of this aid contributed to the Sustainable Development Goals (SDGs)? And to what SDG in particular? This paper present a new methodology using machine learning designed to link project-based flows to the Sustainable Development Goals. It provide first estimates of DAC and non-DAC donors' aid contribution for the goal and show that similar analysis can be done at the recipient level and for other type of textual database such as private sector reports; opening wide array for policy analysis. The methodology presented in this working paper uses semantic analysis of the text description of each project present in the Creditor Reporting System (CRS) |
| Beschreibung: | 1 Online-Ressource (58 Seiten) |
| DOI: | 10.1787/4bdaeb8c-en |
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| spelling | Pincet, Arnaud Verfasser aut Linking Aid to the Sustainable Development Goals - a machine learning approach Arnaud Pincet, Shu Okabe and Martin Pawelczyk Paris OECD Publishing 2019 1 Online-Ressource (58 Seiten) txt rdacontent c rdamedia cr rdacarrier OECD Development Co-operation Working Papers Official Development Assistance amounted USD 146.6 billions in 2017 but do we know how much of this aid contributed to the Sustainable Development Goals (SDGs)? And to what SDG in particular? This paper present a new methodology using machine learning designed to link project-based flows to the Sustainable Development Goals. It provide first estimates of DAC and non-DAC donors' aid contribution for the goal and show that similar analysis can be done at the recipient level and for other type of textual database such as private sector reports; opening wide array for policy analysis. The methodology presented in this working paper uses semantic analysis of the text description of each project present in the Creditor Reporting System (CRS) Development Okabe, Shu ctb Pawelczyk, Martin ctb https://doi.org/10.1787/4bdaeb8c-en Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Pincet, Arnaud Linking Aid to the Sustainable Development Goals - a machine learning approach Development |
| title | Linking Aid to the Sustainable Development Goals - a machine learning approach |
| title_auth | Linking Aid to the Sustainable Development Goals - a machine learning approach |
| title_exact_search | Linking Aid to the Sustainable Development Goals - a machine learning approach |
| title_exact_search_txtP | Linking Aid to the Sustainable Development Goals - a machine learning approach |
| title_full | Linking Aid to the Sustainable Development Goals - a machine learning approach Arnaud Pincet, Shu Okabe and Martin Pawelczyk |
| title_fullStr | Linking Aid to the Sustainable Development Goals - a machine learning approach Arnaud Pincet, Shu Okabe and Martin Pawelczyk |
| title_full_unstemmed | Linking Aid to the Sustainable Development Goals - a machine learning approach Arnaud Pincet, Shu Okabe and Martin Pawelczyk |
| title_short | Linking Aid to the Sustainable Development Goals - a machine learning approach |
| title_sort | linking aid to the sustainable development goals a machine learning approach |
| topic | Development |
| topic_facet | Development |
| url | https://doi.org/10.1787/4bdaeb8c-en |
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